AI Outcome Customer Engineer, Fde, Google Cloud

Google Google · Big Tech · Mumbai, Maharashtra, India +1

Customer-facing AI engineer focused on driving adoption and deployment of Google Cloud enterprise AI solutions, including models, agents, and connectors, into customer environments. Responsibilities include technical leadership, solution design, integration, debugging, and acting as a liaison between customers and product/engineering teams.

What you'd actually do

  1. Partner with Account teams and practice Customer Engineer (CEs) during technical evaluation phases to assess project feasibility, shape proposals for long-term adoption, and validate FDE engagement requests.
  2. Lead upfront technical design for enterprise-grade AI solutions, ensuring seamless and secure integration of models, agents, and connectors into existing customer data pipelines, identity providers, and compliance boundaries.
  3. Dive into code-level context to diagnose and resolve complex customer implementation issues, identify core product bugs, and test workarounds to clear execution roadblocks.
  4. Serve as the definitive liaison to core Product and Engineering teams, troubleshooting systemic deployment blockers and translating real-world field feedback into actionable feature requests.
  5. Steer implementation strategy through technical authority and architectural foresight while owning the technical reality of delivery alongside customer-facing teams.

Skills

Required

  • Experience leading technical delivery strategies
  • debugging systems
  • interfacing with product/engineering organizations
  • Experience with enterprise integrations (APIs, enterprise content management (ECMs), identity)
  • Cloud infrastructure
  • AI/ML model deployments
  • reading or debugging code in a general purpose coding language (e.g., Java, Python, JavaScript, etc.)
  • system design or orchestration frameworks

Nice to have

  • Experience orchestrating specialized technical resources to execute complex builds
  • Experience engaging with, presenting to, and influencing technical stakeholders or executive leaders
  • Ability to dive deep into novel technical problems, decipher extreme ambiguity, diagnose bugs, and emerge with credible architectural solutions
  • Excellent executive communication skills, capable of translating deep technical integration issues into business impact

What the JD emphasized

  • AI deployment plan
  • AI solution engineering
  • enterprise-grade AI solutions
  • complex customer implementation issues
  • systemic deployment blockers
  • technical delivery strategies
  • AI/ML model deployments
  • system design or orchestration frameworks

Other signals

  • drive the adoption of Google Cloud enterprise AI products
  • AI deployment plan
  • applied artificial intelligence engineering
  • AI solution engineering
  • deployment of enterprise AI solutions and use cases